A FRAMEWORK FOR MOBILE VIDEO STREAMING AND VIDEO SHARING IN CLOUD

Journal for Research| Volume 02 | Issue 08 | October 2016 ISSN: 2395-7549

A Framework for Mobile Video Streaming and Video Sharing in Cloud Prabhu K Assistant Professor Department of Computer Science & Engineering V.S.B College of Engineering Technical Campus, Coimbatore

Prabhu R Assistant Professor Department of Computer Science & Engineering V.S.B College of Engineering Technical Campus, Coimbatore

Krishna Kumar R Assistant Professor Department of Computer Science & Engineering V.S.B College of Engineering Technical Campus, Coimbatore

Sangeetha S Associate Professor Department of Computer Science & Engineering V.S.B College of Engineering Technical Campus, Coimbatore

Abstract The transmission of data has grown over years in all the streams of technology. Video and image data plays a very important position in communication around the globe. The usage of Medias over mobile devices had exploded years ago in technology.

However, the usage of traditional network connecting protocols and the service providers are providing lack of quality in services. As the number of users who uses mobile phones is increasing day by day the video traffic over network is also increasing thereby causes disruption in the service which is caused by low bandwidth. Due to this disruption the wireless cannot able to satisfy the users demand for video streaming which eventually causes long buffering time. Influencing cloud computing knowledge to gain advantage over this issue we suggest two solutions. i) Mobile Video Streaming (MoV) and Social Video Sharing (SoV). MoV will create a private cloud for each mobile user which adjusts the bit rate based on return value using scalable video coding technique to improve the scalability and efficient utilization of bandwidth. SoV uses the agent to pre fetch the video data for effective sharing and to reduce the buffering time.

Keywords: Cloud Computing, Mobile Video Streaming, Video Coding, H.264/AVC _______________________________________________________________________________________________________ I.

INTRODUCTION

The more innovations that were made in the wireless network made the people of this era to make use of wireless phones which results in traffic in the network. As many people are preferring video streaming over mobile phones it is challenging for the service providers to provide quality of video service in wireless than in the wired network. In addition, the number of bits stored and transmitted some five years back is tripled in the recent year which eventually causes traffic in the wireless network. The mobile users often suffer from disruptions and long buffering due to low bandwidth and the fluctuations in the 3G/4G wireless network. Mobile cloud computing provides with the expected advantages like large storage capacity, high bandwidth, increase in the QoS, performance. Even then the traffic is accounted by video streaming and mobility. Hence, recently there have been several studies on how to improve the service quality of mobile video streaming on two aspects i) scalability–mobile video streaming should support wide spectrum of mobile devices. It should support different video resolutions with different computations on various wireless links such as 3G and LTE. ii) Adaptability - the link has to adjust the video bit rate adapting to the currently time-varying available link bandwidth of each mobile user. Cloud constructs a private agent for each mobile user in cloud surroundings, which is used by its two ways: Mobile video streaming and social video sharing. By facilitating a 2-tier structure cloud supports efficient video sharing. First tier is a content delivery network and second tier is a data center.

II. RELATED WORK In the adaptive video streaming the speed of the video streaming is based on the traffic and the link’s variation in the bandwidth.

This streaming can be done in two ways. Primarily on the server side where Microsoft’s streaming is the primary one. It is a flat or smooth streaming which dangles between the disparate bit streams and the configurable bit stream and video resolution at the server end while the client dynamically request for the video by locally monitoring the link’s quality. Apple and Adobe are the live adaptive client side streaming which uses the latter way of streaming. The stereotypical streaming is measured by the conventional rate of bandwidth in the stable internet. The mobile video stream can be disrupted due to packet failure and bandwidth misuse. Concerning price adaptation calculating method, TCP friendly rate control is projected for providing streaming services over the mobile systems. Where TCP throughput stream is calculated as a function of packet loss rate round trip time, and packet size. To achieve effective streaming H.264/ASV is collaborated with the extension SVC.

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A Framework for Mobile Video Streaming and Video Sharing in Cloud (J4R/ Volume 02 / Issue 08 / 004)

Fig. 1: Comparison of traditional video streaming and SVC

But it is tested only on the LTE network. Concerning the performance of SVC cloud stream mainly forth put the high quality streaming videos through cloud based SVC proxy.

Mobile Cloud Computing Techniques The cloud computing has been well positioned to provide video streaming services, especially in the wired Internet because of its scalability and capability. To extent the cloud service to mobile phones through wireless networks we need to consider some factors like mobility, storage, and wireless dynamics. Moreover, many recent developments are made in the design of cloud computing which are in charge of satisfying the individual user’s requirement.

III. EXISTING SYSTEM The SVC is used for video conferencing as well as for mobile to broadcast video and for editing applications. Saving different versions of same video may create overhead in the means of storage and communication. To overcome this, we use SVC with H.264/AVC. In the existing system when the user types the URL in the mobile browser the page navigates to the respective URL. If that page has a video embedded in the URL it starts to stream based on the strength of the signal it gets streamed. If the resolution is high or HD it will take long time to stream or gets paused. In the mentioned situation there is delay in the requested video. The drawbacks of these issues are it always uses it uses maximum link capacity for streaming and it cannot control the resolution. In case of weak signal user cannot see the video maintaining consistency as it gets paused on the screen till video streams.

IV. PROPOSED SYSTEM Keeping above objectives in mind we are proposing dubbed AMES cloud in this system to achieve adaptive video streaming.

AMES cloud provides a private agent in cloud for each mobile user. The private agent of a mobile user keeps track of the feedback information about the status of link. In the cloud Platform the private agents are initiated dynamically and optimized.

Also the real-time scalable video coding is done on the cloud computing side efficiently.

The streaming of video and storing it in the cloud is known as video cloud (VC). Within the video cloud there is a base to store the most popular videos called video base (VB). Temp base is a kind of VB which is specifically used to catch the popular videos only for new mobile users. It uses a special counter to count the access frequency of each video. VC will execute a collector to identify the most popular videos in the Video Service Provider (VSP) and encode it in the SVC format and it will get saved in temp VB. The sub VC is created in order to line up the demand for the video from the mobile users. Sub VB is implemented inside the sub VC to provide the most recently accessed video in the cache. If the user requests for a video which is not in the VB or sub VB it is the responsibility of VC or sub VC to fetch, encode and move the video to the mobile under streaming.

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A Framework for Mobile Video Streaming and Video Sharing in Cloud (J4R/ Volume 02 / Issue 08 / 004)

Fig. 2: Illustration of proposed framework

During the time of the streaming of videos, the mobile of the user will automatically send the report on the link condition to the sub VC and it will offer adaptive streams. The temporary storage which is unique in AMES framework is local video base (local VB) which is used for prefetching and buffering.

V. CONCLUSION AND FUTURE WORK A In this paper we have discussed about our proposal on AMoV which is achieved through sharing the AMES framework using private agent in the cloud. VC and sub VC stores the video to provide “Non terminating” video which are explicitly used to adapt to the fluctuations in the link capacity. It also provides “Non Buffering” service with the help of VB, sub VB and local VB based on Scalable Video Coding technique. The main objective of the paper is to know how the cloud helps the video streaming in mobile to achieve adaptability and scalability. But this paper turns a blind eye towards cost while encoding. To overcome this detract we propose large scale implementation primarily considering cost and power. We try to improve prefetching and also to conclusion section is not required. Although a conclusion may review the main points of the paper, do not replicate the abstract as the conclusion. A conclusion might elaborate on the importance of the work or suggest applications and extensions.

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